TimeNet unifies diverse time-series data for better temporal models
TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models
Artificial IntelligenceMachine Learning
Summary
Temporal Foundation Models aim to understand data that changes over time across many different areas, but building these models has been hard because every dataset often uses different formats and rules. The authors created TimeNet, a shared data system that brings together many types of time-related data in one common format, supporting different kinds of signals and tasks. This makes it easier to combine and train models on many datasets at once. Using TimeNet, their model improved accuracy by 14% compared to training on just one dataset at a time.
What this means in practice
- •For machine learning engineers: Integrate diverse time-series datasets into a unified pipeline to train temporal models more effectively without format conversion.
- •For healthcare analytics teams: Combine varied patient monitoring data with irregular timing into one system for improved cross-domain temporal analysis.
Authors
Martin Maritsch, Timo Stoffregen, Thomas Kaar, Behsad Riemer, Maxwell A. Xu, Max Rosenblattl, Juncheng Liu, Nicolas Zumarraga, Yu Yvonne Wu, Denys Herasymuk, Sparsh Rastogi, Hyungjun Yoon, Bosong Huang, Arvind Pillai, Dmytro Lopushanskyy, Tony Chen, Robin Deuber, Yichen Liu, Shvat Messica, Dan Li, Jian Lou, Yuwei Zhang, Jaeho Kim, Renée Rosillo Garcia, Fan Wu, Robert Müller, Elgar Fleisch, Flora D. Salim, Dimitris Spathis, Yuzhe Yang, Aaqib Saeed, Daniel McDuff, Ming Jin, Markus Kreft, Kevin O'Sullivan, Robert Jakob, Azul Garza, Paul Schmiedmayer, Patrick Langer
Abstract
Temporal Foundation Models (TFMs) aim to generalize across domains, datasets, and tasks. Yet, their development remains constrained by fragmented, task-specific data formats, annotations, and processing pipelines. We introduce TimeNet, an open-source data standard and scalable infrastructure that decouples temporal data from task definitions and represents signals, metadata, annotations, and supervision in a shared, extensible data model. TimeNet supports multimodal signals with regular, irregular, or ordinal time axes and expresses different task families (including classification, forecasting, temporal localization, question answering, generation, and editing) as reusable views over the same recordings. This shared representation enables heterogeneous time-series datasets to be combined for large-scale model training across domains, modalities, and tasks. We demonstrate TimeNet by transcoding datasets with 1.5M task instances spanning diverse domains, modalities, temporal scales, and forms of supervision, while retaining practical I/O performance relative to native formats. TimeNet enables an existing TFN training pipeline to support joint training on a configurable number of heterogeneous datasets through configuration changes alone. We show this capability by training TFM across multiple datasets and tasks, obtaining a 14% F1 score improvement compared with models trained on individual datasets. These results show that TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.